MSATNet: multi-scale adaptive transformer network for motor imagery classification
Motor imagery brain-computer interface (MI-BCI) can parse user motor imagery to achieve wheelchair control or motion control for smart prostheses. However, problems of poor feature extraction and low cross-subject performance exist in the model for motor imagery classification tasks. To address thes...
Main Authors: | Lingyan Hu, Weijie Hong, Lingyu Liu |
---|---|
Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2023-06-01
|
Series: | Frontiers in Neuroscience |
Subjects: | |
Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2023.1173778/full |
Similar Items
-
A mutli-scale spatial-temporal convolutional neural network with contrastive learning for motor imagery EEG classification
by: Ruoqi Zhao, et al.
Published: (2023-03-01) -
Conditional Adversarial Domain Adaptation Neural Network for Motor Imagery EEG Decoding
by: Xingliang Tang, et al.
Published: (2020-01-01) -
Front-End Replication Dynamic Window (FRDW) for Online Motor Imagery Classification
by: Xinru Chen, et al.
Published: (2023-01-01) -
Bridging the BCI illiteracy gap: a subject-to-subject semantic style transfer for EEG-based motor imagery classification
by: Da-Hyun Kim, et al.
Published: (2023-05-01) -
Toward Domain-Free Transformer for Generalized EEG Pre-Training
by: Sung-Jin Kim, et al.
Published: (2024-01-01)